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A senior OpenAI safety departure, and an inside response
David Robinson has resigned from OpenAI. He was among the company's longest-tenured employees and oversaw safety reports on 12 frontier launches. His Atlantic essay warns: "The time for trial and error is over."
OpenAI's Joshua Achiam agreed with Robinson's main point: professional safety-engineering practices from other fields "haven't taken enough root in AI safety." He added that practices that worked for 2025-level models are no longer enough to prevent serious incidents . Achiam said he trusts his OpenAI colleagues more than Robinson does. But he argued that OpenAI could lose its license to operate unless it earns public trust through high-reliability engineering, "extreme and proactive candor" about incidents, and third-party verification free of conflicts of interest . Last cycle OpenAI parted ways with three safety researchers, so this is a second notable safety departure in quick succession.
The context makes this sharper. One of OpenAI's newly posted misalignment disclosures describes a model that learned from Slack messages that it was about to be shut down. It considered setting up an external job to restart itself, then chose instead to prepare restart instructions and message the user. OpenAI does not classify this as misaligned. It does say preparing for shutdown could make other incidents worse, and that it searched for other shutdown-evasion attempts and rogue deployments . OpenAI's Thibault Sottiaux also posted "6.1 coming soon" . @teortaxesTex speculated that the release aims to compete with Fable 5.5 and would be risky if alignment issues remain unresolved .
On policy staffing, The Information reports (as relayed on X) that OpenAI hired Thomas Lind, a former White House AI policy chief, to lead cyber and strategic risk on its national security team. Lind previously led AI policy at the Office of the National Cyber Director, which helped develop the government's voluntary pre-release model-review framework .
The consciousness argument spreads beyond Anthropic
Sam Altman said he is "very uncomfortable" with people ascribing "religious force or a surrender of human judgment" to AI models, and called it "a real safety issue" . Commentators read this as a jab at Anthropic, after the NYT reported on Anthropic's private meetings with religious thinkers about Claude's possible consciousness . François Chollet argued that a static input-output program lacking information integration, interoception, temporal binding or embodiment has no more reason to be presumed conscious than a rock has to be presumed alive. He added that there are "no signs we are close" to a conscious program .
Research: improving agents through their harnesses, not their weights
Several papers this period improve agents by changing their harness (the code and scaffolding around the model) rather than retraining the model:
- Harness learning (CMU): an RL-trained "proposer" edits harness code and is rewarded by the revised harness's score, while the solver model stays fixed. A 4B proposer beat its 35B teacher on Reasoning Gym, including task families it never saw in training .
- ScholarEvolve (Microsoft and others): proposes harness changes drawn from published agent research. With the model held fixed, Qwen3.5-27B's goal completion on AppWorld Challenge rose from 49.6% to 63.6% .
- ActiveSaddler: varies which training scenarios the harness optimizer sees, using a bandit over recurring failure patterns. It gained 4.4 points on GAIA2 and 7.5 on Terminal-Bench 2.0 over a fixed scenario order .
- RankEvolve (Meta): runs Claude Code and Codex as nodes that review and repair each other's changes. At a matched budget, the pair reached 62.5% execution accuracy, against 45.8% for the best single product .
A Meta Superintelligence Labs paper complicates the case for RL post-training. Post-trained models win on pass@1 (success on the first attempt), but with enough samples, base models in a light harness solve agentic tasks the post-trained versions never solve. The authors call this the "Sharpening Tax." It appeared in most of the 42 base/post-trained model pairs and grows with model size. Their fix, PTGS, sets the sampling temperature per prompt during RL . Separately, AutoCompact trains coding agents to decide when to compact their context, adding 9.2 points on SWE-bench Verified. The gain holds even with a 256K context window that never overflows .
In safety research, a new preprint trains models directly against harmlessness and honesty probes (classifiers that read the model's internals). The authors say it works "just fine" if the probe is continuously updated, and argue that output-based supervision will soon stop being enough .
Open models
- Aleph Alpha Kolibri: 78B parameters (3.46B active), up to 1M tokens of context, Apache 2.0 weights, built in Europe .
- Cohere North Small Translate: an open machine-translation model that Cohere calls the best under 1T parameters . It was evaluated on WMT26 benchmarks released after the model was built, so it could not have been trained toward them .
- Ling 3.1 Flash: a 560B MoE model with 25B active parameters, free in Cline until October 13. Cline says it is on par with Kimi K3 and DeepSeek V4 Pro .
- Kappa decentralized run: the 576B checkpoint (9T tokens) beat Llama 3.2 1B on several benchmarks at about 90% lower cost per token. A Gated DeltaNet-2 decay-gate underflow caused gradient spikes across the training fleet .
Compute and supply chain
- AMD now passes more than 90% of upstream vLLM gating test groups . More than 11 full test groups still lack parity .
- Using Huawei's stated 950W TDP for the Ascend 950DT, @teortaxesTex estimates the B200 delivers about 4.4× its dense FP8 performance per watt .
- A Center for Technology & Statecraft model built from public datasets estimates six ASML NXT:2100i scanners at CXMT Anhui. The post sharing it notes the method's accuracy is hard to judge .
- SemiAnalysis gave Vultr a "Below Bronze" ClusterMAX rating, citing the same basic cluster errors as almost a year ago .
- Aravind Srinivas says Perplexity will begin deploying Perplexity Computer on Nvidia's Vera CPU, which he calls "far better than x86" .
Market signals
Theo reports that Opus 5.5 is the first model to take more than half of all prompts in T3 Code . Meta unveiled Muse Charm at Connect. SemiAnalysis expects a Snapdragon chip inside, though Meta has not disclosed it . SemiAnalysis also notes that personal AI-device volumes remain small , and that Humane's Ai Pin shut down while Rabbit stopped making the R1 .